The Effect of Poor Environmental Impact Assessment (EIA) Implementation on the Wellbeing of the KwaMathukuza Community, Newcastle Municipality in South Africa
Bibliographic record
Abstract
This study investigated the possible health impacts a waste water treatment plant (WWTP) will have on a community that reside near it. The study area was a low cost housing residential area within the Newcastle municipality in South Africa due to its close proximity to a WWTP. The data was acquired through informal interviews, questionnaires and observations. The participants were recruited mainly from the residents who resides about 5.0 km from the plant, local health caregivers, municipality official and the local government management. A survey of the study area showed no other possible source of the odorous gases except the WWTP. About 97.0% of respondents have smelt the bad odour that is probably released from the plant. The results also indicates that a significant number of people suffer from headaches, vision, olfactory and breathing problems which could be linked to the nearby WWTP. It was also discovered that the respondents who are at a distance of more than 5.0 km from the WWTP were also negatively impacted by the gases as the residents who are within 5.0 km. Looking into the future, every development needs to follow the proper procedure of EIA to reduce negative impact on human health. It also means that governments should review the buffer distances between such facilities industry and human settlements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".